Medical images are rendered using algorithms and other processes that depend on a large number of parameters. It is often difficult, even for the most experienced user, to understand how each parameter affects the rendering and, ultimately, the visualization of the underlying medical image data. As a result, there is a need to navigate the parameter space in a meaningful and efficient manner to identify the best set of parameter values for a desired visualization.
The challenges of exploring the large parameter spaces of the rendering parameters are often addressed by well-designed traditional user interfaces and, more recently, machine learning approaches that simplify the parameter specification. Collections of parameters may be grouped into presets and presented to the user via reference images or category descriptions. Some conventional systems present presets for sub-groups of parameters to the user, allowing for a guided approach to the parameter selection. However, each of these approaches only allows for limited interaction with the user. Thus, it is desired to provide a more immersive interface for exploring the parameter space that allows for greater user interaction and targeting of sub-groups of parameters.